This report describes the results of a preregistered study available at: https://osf.io/w46r9.
Note also that this data has been cleaned beforehand. Five datasets
were merged (joined) through an inner join—3 Qualtrics surveys and 2
Inquisit tasks—so as to keep only participants who at least participated
at each step of the study. Missing data will be imputed later on.
Duplicates were addressed with the rempsyc::best_duplicate
function, which keeps the duplicate with the least amount of missing
values, and in case of ties, takes the first occurrence.
library(rempsyc)
library(dplyr)
library(interactions)
library(performance)
library(see)
library(report)
library(datawizard)
library(bestNormalize)
library(psych)
library(visdat)
library(missForest)
library(doParallel)
summary(report(sessionInfo()))The analysis was done using the R Statistical language (v4.2.2; R Core Team, 2022) on Windows 10 x64, using the packages iterators (v1.0.14), doParallel (v1.0.17), interactions (v1.1.5), performance (v0.10.2.5), see (v0.7.4.3), report (v0.5.6), foreach (v1.5.2), datawizard (v0.6.5.15), bestNormalize (v1.9.0), psych (v2.2.9), missForest (v1.5), rempsyc (v0.1.1.2), visdat (v0.5.3) and dplyr (v1.1.0).
# Read data
data <- read.table("data/fulldataset.txt", sep = "\t", header = TRUE)
# Code group variable as factor
data <- data %>%
mutate(condition_dum = ifelse(condition == "Mindfulness", 1, 0),
condition = as.factor(condition))
# Dummy variable (instead of factor) is required by the `interact_plot()` function...
cat(report_participants(data, threshold = 1))475 participants (Gender: 57.5% women, 40.8% men, 1.26% non-binary, 0.42% missing; Country: 99.16% United States of America, 0.84% other; Race: 77.26% White, 11.79% Black or African American, 4.21% Asian, 3.16% Mixed, 1.26% American Indian or Alaska Native, 2.32% other)
# Allocation ratio
report(data$condition)x: 2 levels, namely Control (n = 236, 49.68%) and Mindfulness (n = 239, 50.32%)
In this section, we are preparing the data for analysis: (a) taking
care of preliminary exclusions, (b) checking for and exploring missing
values, (d) imputing missing data with missForest, (e)
computing scale means, and (f) extracting reliability indices for our
scales.
First, we only want to keep those who agreed to keep their participation in the study after the debriefing.
data %>%
count(debriefing)| debriefing | n |
|---|---|
| Yes, I accept to maintain my participation to this study. | 473 |
| NA | 2 |
Nobody to exclude based on consent (nobody asked to be removed).
Second, we know that we only want to keep participants who had at
least an 80% success rate in the critical experimental manipulation
task. Let’s see how many participants have less than an 80% success
rate. Those with missing values for variable
manipsuccessleft will also be excluded since they have not
completed the critical experimental manipulation in this study.
data %>%
summarize(success.80 = sum(manipsuccessleft < .80,
na.rm = TRUE),
is.na = sum(is.na(manipsuccessleft)))| success.80 | is.na |
|---|---|
| 34 | 0 |
There’s 34 people with success smaller than 80%, let’s exclude them.
data <- data %>%
filter(manipsuccessleft >= .80)
cat(report_participants(data, threshold = 1))441 participants (Gender: 58.0% women, 40.1% men, 1.36% non-binary, 0.45% missing; Country: 99.09% United States of America, 0.91% other; Race: 77.32% White, 11.11% Black or African American, 4.31% Asian, 3.40% Mixed, 1.36% American Indian or Alaska Native, 2.49% other)
Let’s also exclude those who failed 2 or more attention checks (i.e., keep with those with a score of two or more).
data <- data %>%
mutate(att_check = rowSums(
select(., att_check1, att_check2, att_check3)))
data %>%
count(att_check)| att_check | n |
|---|---|
| 0 | 2 |
| 1 | 3 |
| 2 | 18 |
| 3 | 416 |
| NA | 2 |
There’s 5 more exclusions here.
data <- data %>%
filter(att_check >= 2)
cat(report_participants(data, threshold = 1))434 participants (Gender: 58.3% women, 40.3% men, 1.38% non-binary; Country: 99.54% United States of America, 0.46% other; Race: 77.65% White, 11.29% Black or African American, 4.15% Asian, 3.46% Mixed, 1.38% American Indian or Alaska Native, 2.07% other)
# Check for nice_na
nice_na(data, scales = c("BSCS", "BAQ", "KIMS"))| var | items | na | cells | na_percent | na_max | na_max_percent | all_na |
|---|---|---|---|---|---|---|---|
| BSCS_1:BSCS_7 | 7 | 0 | 3038 | 0.00 | 0 | 0.00 | 0 |
| BAQ_1:BAQ_12 | 12 | 0 | 5208 | 0.00 | 0 | 0.00 | 0 |
| KIMS_1:KIMS_39 | 39 | 0 | 16926 | 0.00 | 0 | 0.00 | 0 |
| Total | 276 | 35587 | 119784 | 29.71 | 83 | 30.07 | 0 |
No missing data for our scales of interest, yeah! Except 2 items for KIMS.
Let’s check for patterns of missing data.
# Smaller subset of data for easier inspection
data %>%
select(manualworkerId:att_check) %>%
vis_miss# Let's use Little's MCAR test to confirm
# We have to proceed by "scale" because the function can only
# support 30 variables max at a time
library(naniar)
data %>%
select(BSCS_1:BSCS_7) %>%
mcar_test| statistic | df | p.value | missing.patterns |
|---|---|---|---|
| 0 | 0 | 0 | 1 |
# a p-value of 0 means the test failed because there's no missing values.
data %>%
select(BAQ_1:BAQ_12) %>%
mcar_test| statistic | df | p.value | missing.patterns |
|---|---|---|---|
| 0 | 0 | 0 | 1 |
# a p-value of 0 means the test failed because there's no missing values.
data %>%
select(KIMS_1:KIMS_20) %>%
mcar_test| statistic | df | p.value | missing.patterns |
|---|---|---|---|
| 0 | 0 | 1 | 1 |
# a p-value of 0 means the test failed because there's no missing values.
data %>%
select(KIMS_21:KIMS_39) %>%
mcar_test| statistic | df | p.value | missing.patterns |
|---|---|---|---|
| 0 | 0 | 0 | 1 |
# a p-value of 0 means the test failed because there's no missing values.Here, we impute missing data with the missForest
package, as it is one of the best imputation methods.
# Need character variables as factors
# "Error: Can not handle categorical predictors with more than 53 categories."
# So we have to temporarily remove IDs also...
new.data <- data %>%
select(-c(manualworkerId, embeddedworkerId,
att_check1, att_check2, att_check3)) %>%
mutate(across(where(is.character), as.factor))
# Parallel processing
registerDoParallel(cores = 4)
# Variables
set.seed(100)
data.imp <- missForest(new.data, verbose = TRUE, parallelize = "variables")## removed variable(s) 127 129 137 151 154 158 163 173 176 183 193 203 208 264 due to the missingness of all entries
## parallelizing over the variables of the input data matrix 'xmis'
## missForest iteration 1 in progress...done!
## estimated error(s): NaN 0.1445299
## difference(s): 0.00002129105 0.08262014
## time: 12.52 seconds
##
## missForest iteration 2 in progress...done!
## estimated error(s): NaN 0.1445299
## difference(s): 0.00007110337 0.08689928
## time: 12.8 seconds
# Total time is 2 sec (4*0.5) - 4 cores
# Extract imputed dataset
new.data <- data.imp$ximpThere are some variables we don’t actually want to impute, like country. We want to keep those NAs in that case. Let’s add them back. We also want to add ID back.
# Add ID
new.data <- bind_cols(manualworkerId = data$manualworkerId, new.data)
# Add back the NAs in country, attention checks, etc.
data <- new.data %>%
mutate(country.ip = data$country.ip,
gender = data$gender,
att_check1 = data$att_check1,
att_check2 = data$att_check2,
att_check3 = data$att_check3)Why impute the data? van Ginkel explains,
Regardless of the missingness mechanism, multiple imputation is always to be preferred over listwise deletion. Under MCAR it is preferred because it results in more statistical power, under MAR it is preferred because besides more power it will give unbiased results whereas listwise deletion may not, and under NMAR it is also the preferred method because it will give less biased results than listwise deletion.
van Ginkel, J. R., Linting, M., Rippe, R. C. A., & van der Voort, A. (2020). Rebutting existing misconceptions about multiple imputation as a method for handling missing data. Journal of Personality Assessment, 102(3), 297-308. https://doi.org/10.1080/00223891.2018.1530680
Why missForest? It outperforms other imputation methods,
including the popular MICE (multiple imputation by chained equations).
You also don’t end up with several datasets, which makes it easier for
following analyses. Finally, it can be applied to mixed data types
(missings in numeric & categorical variables).
Waljee, A. K., Mukherjee, A., Singal, A. G., Zhang, Y., Warren, J., Balis, U., … & Higgins, P. D. (2013). Comparison of imputation methods for missing laboratory data in medicine. BMJ open, 3(8), e002847. https://doi.org/10.1093/bioinformatics/btr597
Stekhoven, D. J., & Bühlmann, P. (2012). MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1), 112-118. https://doi.org/10.1093/bioinformatics/btr597
# Reverse code items 2, 4, 6, 7
data <- data %>%
mutate(across(starts_with("BSCS"), .names = "{col}r"))
data <- data %>%
mutate(across(c(BSCS_2, BSCS_4, BSCS_6, BSCS_7), ~nice_reverse(.x, 5), .names = "{col}r"))
# Get mean BSCS
data <- data %>%
mutate(BSCS = rowMeans(select(., BSCS_1r:BSCS_7r)))# Reverse code item 7
data <- data %>%
mutate(across(starts_with("BAQ"), .names = "{col}r"))
data <- data %>%
mutate(across(BAQ_7, ~nice_reverse(.x, 7), .names = "{col}r"))
# Get sum of BAQ
data <- data %>%
mutate(BAQ = rowMeans(select(., BAQ_1r:BAQ_12r)))# Reverse code items 3-4, 8, 11-12, 14, 16, 18, 20, 22, 23-24, 27-28, 31-32, 35-36
data <- data %>%
mutate(across(starts_with("KIMS"), .names = "{col}r"))
data <- data %>%
mutate(across(all_of(paste0("KIMS_", c(3:4, 8, 11:12, 14, 16, 18, 20,
22:24, 27:28, 31:32, 35:36))),
~nice_reverse(.x, 5), .names = "{col}r"))
# Get sum of KIMS
data <- data %>%
mutate(KIMS = rowMeans(select(., KIMS_1r:KIMS_39r)))# labels.part3$SHS_22
# SHS: forgot to add back the two other scales!!!!!
# So no reverse scoring needed.
# Get sum of SHS and subscales
data <- data %>%
mutate(SHS = rowMeans(select(., SHS_1:SHS_21)),
SHS.mean = rowMeans(select(., SHS_1:SHS_14)),
SHS.aggravation = rowMeans(select(., SHS_14:SHS_21)))data %>%
select(BSCS_1r:BSCS_7r) %>%
omega(nfactors = 1)## Loading required namespace: GPArotation
## Omega_h for 1 factor is not meaningful, just omega_t
## Warning in schmid(m, nfactors, fm, digits, rotate = rotate, n.obs = n.obs, :
## Omega_h and Omega_asymptotic are not meaningful with one factor
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.83
## G.6: 0.84
## Omega Hierarchical: 0.83
## Omega H asymptotic: 1
## Omega Total 0.84
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* h2 u2 p2
## BSCS_1r 0.71 0.51 0.49 1
## BSCS_2r 0.70 0.49 0.51 1
## BSCS_3r 0.58 0.33 0.67 1
## BSCS_4r 0.70 0.49 0.51 1
## BSCS_5r 0.46 0.21 0.79 1
## BSCS_6r 0.76 0.58 0.42 1
## BSCS_7r 0.61 0.37 0.63 1
##
## With Sums of squares of:
## g F1*
## 3 0
##
## general/max 26806166228882828 max/min = 1
## mean percent general = 1 with sd = 0 and cv of 0
## Explained Common Variance of the general factor = 1
##
## The degrees of freedom are 14 and the fit is 0.48
## The number of observations was 434 with Chi Square = 207.35 with prob < 0.0000000000000000000000000000000000017
## The root mean square of the residuals is 0.09
## The df corrected root mean square of the residuals is 0.11
## RMSEA index = 0.178 and the 10 % confidence intervals are 0.158 0.2
## BIC = 122.32
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 14 and the fit is 0.48
## The number of observations was 434 with Chi Square = 207.35 with prob < 0.0000000000000000000000000000000000017
## The root mean square of the residuals is 0.09
## The df corrected root mean square of the residuals is 0.11
##
## RMSEA index = 0.178 and the 10 % confidence intervals are 0.158 0.2
## BIC = 122.32
##
## Measures of factor score adequacy
## g F1*
## Correlation of scores with factors 0.92 0
## Multiple R square of scores with factors 0.85 0
## Minimum correlation of factor score estimates 0.70 -1
##
## Total, General and Subset omega for each subset
## g F1*
## Omega total for total scores and subscales 0.84 0.83
## Omega general for total scores and subscales 0.83 0.83
## Omega group for total scores and subscales 0.00 0.00
data %>%
select(BAQ_1r:BAQ_12r) %>%
omega(nfactors = 1)## Omega_h for 1 factor is not meaningful, just omega_t
## Warning in schmid(m, nfactors, fm, digits, rotate = rotate, n.obs = n.obs, :
## Omega_h and Omega_asymptotic are not meaningful with one factor
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.85
## G.6: 0.88
## Omega Hierarchical: 0.85
## Omega H asymptotic: 1
## Omega Total 0.85
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* h2 u2 p2
## BAQ_1r 0.66 0.43 0.57 1
## BAQ_2r 0.57 0.33 0.67 1
## BAQ_3r 0.69 0.48 0.52 1
## BAQ_4r 0.26 0.07 0.93 1
## BAQ_5r 0.49 0.24 0.76 1
## BAQ_6r 0.70 0.50 0.50 1
## BAQ_7r 0.47 0.23 0.77 1
## BAQ_8r 0.70 0.49 0.51 1
## BAQ_9r 0.75 0.57 0.43 1
## BAQ_10r 0.48 0.23 0.77 1
## BAQ_11r 0.49 0.24 0.76 1
## BAQ_12r 0.50 0.25 0.75 1
##
## With Sums of squares of:
## g F1*
## 4 0
##
## general/max 48571401317850552 max/min = 1
## mean percent general = 1 with sd = 0 and cv of 0
## Explained Common Variance of the general factor = 1
##
## The degrees of freedom are 54 and the fit is 1.62
## The number of observations was 434 with Chi Square = 693.9 with prob < 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000063
## The root mean square of the residuals is 0.12
## The df corrected root mean square of the residuals is 0.13
## RMSEA index = 0.165 and the 10 % confidence intervals are 0.155 0.177
## BIC = 365.96
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 54 and the fit is 1.62
## The number of observations was 434 with Chi Square = 693.9 with prob < 0.00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000063
## The root mean square of the residuals is 0.12
## The df corrected root mean square of the residuals is 0.13
##
## RMSEA index = 0.165 and the 10 % confidence intervals are 0.155 0.177
## BIC = 365.96
##
## Measures of factor score adequacy
## g F1*
## Correlation of scores with factors 0.94 0
## Multiple R square of scores with factors 0.88 0
## Minimum correlation of factor score estimates 0.75 -1
##
## Total, General and Subset omega for each subset
## g F1*
## Omega total for total scores and subscales 0.85 0.85
## Omega general for total scores and subscales 0.85 0.85
## Omega group for total scores and subscales 0.00 0.00
data %>%
select(KIMS_1r:KIMS_39r) %>%
omega(nfactors = 1)## Omega_h for 1 factor is not meaningful, just omega_t
## Warning in schmid(m, nfactors, fm, digits, rotate = rotate, n.obs = n.obs, :
## Omega_h and Omega_asymptotic are not meaningful with one factor
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.89
## G.6: 0.94
## Omega Hierarchical: 0.86
## Omega H asymptotic: 0.96
## Omega Total 0.9
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* h2 u2 p2
## KIMS_1r 0.24 0.06 0.94 1
## KIMS_2r 0.69 0.47 0.53 1
## KIMS_3r 0.61 0.37 0.63 1
## KIMS_4r 0.57 0.33 0.67 1
## KIMS_5r 0.30 0.09 0.91 1
## KIMS_6r 0.62 0.38 0.62 1
## KIMS_7r 0.30 0.09 0.91 1
## KIMS_8r 0.01 0.99 1
## KIMS_9r 0.03 0.97 1
## KIMS_10r 0.62 0.39 0.61 1
## KIMS_11r 0.47 0.22 0.78 1
## KIMS_12r 0.59 0.35 0.65 1
## KIMS_13r 0.24 0.06 0.94 1
## KIMS_14r 0.66 0.44 0.56 1
## KIMS_15r 0.43 0.19 0.81 1
## KIMS_16r 0.58 0.33 0.67 1
## KIMS_17r 0.22 0.05 0.95 1
## KIMS_18r 0.68 0.46 0.54 1
## KIMS_19r 0.02 0.98 1
## KIMS_20r 0.30 0.09 0.91 1
## KIMS_21r 0.30 0.09 0.91 1
## KIMS_22r 0.69 0.47 0.53 1
## KIMS_23r 0.61 0.37 0.63 1
## KIMS_24r 0.29 0.08 0.92 1
## KIMS_25r 0.28 0.08 0.92 1
## KIMS_26r 0.48 0.23 0.77 1
## KIMS_27r 0.31 0.10 0.90 1
## KIMS_28r 0.54 0.29 0.71 1
## KIMS_29r 0.29 0.08 0.92 1
## KIMS_30r 0.23 0.05 0.95 1
## KIMS_31r 0.30 0.09 0.91 1
## KIMS_32r 0.62 0.38 0.62 1
## KIMS_33r 0.31 0.09 0.91 1
## KIMS_34r 0.48 0.23 0.77 1
## KIMS_35r 0.51 0.26 0.74 1
## KIMS_36r 0.47 0.22 0.78 1
## KIMS_37r 0.30 0.09 0.91 1
## KIMS_38r 0.31 0.10 0.90 1
## KIMS_39r 0.27 0.07 0.93 1
##
## With Sums of squares of:
## g F1*
## 7.8 0.0
##
## general/max 10257299580827460 max/min = 1
## mean percent general = 1 with sd = 0 and cv of 0
## Explained Common Variance of the general factor = 1
##
## The degrees of freedom are 702 and the fit is 13.55
## The number of observations was 434 with Chi Square = 5670.49 with prob < 0
## The root mean square of the residuals is 0.18
## The df corrected root mean square of the residuals is 0.18
## RMSEA index = 0.128 and the 10 % confidence intervals are 0.125 0.131
## BIC = 1407.21
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 702 and the fit is 13.55
## The number of observations was 434 with Chi Square = 5670.49 with prob < 0
## The root mean square of the residuals is 0.18
## The df corrected root mean square of the residuals is 0.18
##
## RMSEA index = 0.128 and the 10 % confidence intervals are 0.125 0.131
## BIC = 1407.21
##
## Measures of factor score adequacy
## g F1*
## Correlation of scores with factors 0.96 0
## Multiple R square of scores with factors 0.92 0
## Minimum correlation of factor score estimates 0.84 -1
##
## Total, General and Subset omega for each subset
## g F1*
## Omega total for total scores and subscales 0.90 0.86
## Omega general for total scores and subscales 0.86 0.86
## Omega group for total scores and subscales 0.00 0.00
data %>%
select(SHS_1:SHS_21) %>%
omega(nfactors = 2)##
## Three factors are required for identification -- general factor loadings set to be equal.
## Proceed with caution.
## Think about redoing the analysis with alternative values of the 'option' setting.
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.98
## G.6: 0.98
## Omega Hierarchical: 0.82
## Omega H asymptotic: 0.83
## Omega Total 0.98
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* F2* h2 u2 p2
## SHS_1 0.73 0.43 0.71 0.29 0.74
## SHS_2 0.75 0.44 0.76 0.24 0.74
## SHS_3 0.66 0.48 0.68 0.32 0.65
## SHS_4 0.75 0.43 0.75 0.25 0.75
## SHS_5 0.72 0.32 0.64 0.36 0.82
## SHS_6 0.80 0.24 0.23 0.75 0.25 0.85
## SHS_7 0.74 0.34 0.66 0.34 0.82
## SHS_8 0.78 0.35 0.75 0.25 0.82
## SHS_9 0.76 0.48 0.81 0.19 0.71
## SHS_10 0.76 0.46 0.80 0.20 0.73
## SHS_11 0.74 0.28 0.65 0.35 0.85
## SHS_12 0.75 0.46 0.78 0.22 0.73
## SHS_13 0.82 0.33 0.80 0.20 0.84
## SHS_14 0.76 0.24 0.20 0.67 0.33 0.85
## SHS_15 0.79 0.34 0.75 0.25 0.83
## SHS_16 0.70 0.35 0.62 0.38 0.79
## SHS_17 0.78 0.47 0.83 0.17 0.73
## SHS_18 0.75 0.42 0.73 0.27 0.76
## SHS_19 0.75 0.30 0.67 0.33 0.84
## SHS_20 0.76 0.46 0.79 0.21 0.74
## SHS_21 0.76 0.36 0.71 0.29 0.81
##
## With Sums of squares of:
## g F1* F2*
## 11.93 2.53 0.84
##
## general/max 4.71 max/min = 3.01
## mean percent general = 0.78 with sd = 0.06 and cv of 0.07
## Explained Common Variance of the general factor = 0.78
##
## The degrees of freedom are 169 and the fit is 1.47
## The number of observations was 434 with Chi Square = 622.88 with prob < 0.000000000000000000000000000000000000000000000000000033
## The root mean square of the residuals is 0.02
## The df corrected root mean square of the residuals is 0.03
## RMSEA index = 0.079 and the 10 % confidence intervals are 0.072 0.085
## BIC = -403.47
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 189 and the fit is 4.32
## The number of observations was 434 with Chi Square = 1833.36 with prob < 0.0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000072
## The root mean square of the residuals is 0.13
## The df corrected root mean square of the residuals is 0.14
##
## RMSEA index = 0.142 and the 10 % confidence intervals are 0.136 0.148
## BIC = 685.55
##
## Measures of factor score adequacy
## g F1* F2*
## Correlation of scores with factors 0.91 0.70 0.68
## Multiple R square of scores with factors 0.84 0.49 0.47
## Minimum correlation of factor score estimates 0.67 -0.02 -0.07
##
## Total, General and Subset omega for each subset
## g F1* F2*
## Omega total for total scores and subscales 0.98 0.97 0.91
## Omega general for total scores and subscales 0.82 0.78 0.71
## Omega group for total scores and subscales 0.14 0.19 0.20
# PANAS_pos
data %>%
select(paste0("PANAS_", seq(1, 10, 2))) %>%
omega(nfactors = 1)## Omega_h for 1 factor is not meaningful, just omega_t
## Warning in schmid(m, nfactors, fm, digits, rotate = rotate, n.obs = n.obs, :
## Omega_h and Omega_asymptotic are not meaningful with one factor
## Warning in cov2cor(t(w) %*% r %*% w): diag(.) had 0 or NA entries; non-finite
## result is doubtful
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.82
## G.6: 0.81
## Omega Hierarchical: 0.83
## Omega H asymptotic: 1
## Omega Total 0.83
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* h2 u2 p2
## PANAS_1 0.76 0.58 0.42 1
## PANAS_3 0.74 0.55 0.45 1
## PANAS_5 0.90 0.82 0.18 1
## PANAS_7 0.44 0.20 0.80 1
## PANAS_9 0.61 0.37 0.63 1
##
## With Sums of squares of:
## g F1*
## 2.5 0.0
##
## general/max Inf max/min = NaN
## mean percent general = 1 with sd = 0 and cv of 0
## Explained Common Variance of the general factor = 1
##
## The degrees of freedom are 5 and the fit is 0.11
## The number of observations was 434 with Chi Square = 45.82 with prob < 0.0000000099
## The root mean square of the residuals is 0.06
## The df corrected root mean square of the residuals is 0.09
## RMSEA index = 0.137 and the 10 % confidence intervals are 0.103 0.175
## BIC = 15.46
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5 and the fit is 0.11
## The number of observations was 434 with Chi Square = 45.82 with prob < 0.0000000099
## The root mean square of the residuals is 0.06
## The df corrected root mean square of the residuals is 0.09
##
## RMSEA index = 0.137 and the 10 % confidence intervals are 0.103 0.175
## BIC = 15.46
##
## Measures of factor score adequacy
## g F1*
## Correlation of scores with factors 0.94 0
## Multiple R square of scores with factors 0.89 0
## Minimum correlation of factor score estimates 0.77 -1
##
## Total, General and Subset omega for each subset
## g F1*
## Omega total for total scores and subscales 0.83 0.83
## Omega general for total scores and subscales 0.83 0.83
## Omega group for total scores and subscales 0.00 0.00
# PANAS_neg
data %>%
select(paste0("PANAS_", seq(2, 10, 2))) %>%
omega(nfactors = 1)## Omega_h for 1 factor is not meaningful, just omega_t
## Warning in schmid(m, nfactors, fm, digits, rotate = rotate, n.obs = n.obs, :
## Omega_h and Omega_asymptotic are not meaningful with one factor
## Warning in schmid(m, nfactors, fm, digits, rotate = rotate, n.obs = n.obs, :
## diag(.) had 0 or NA entries; non-finite result is doubtful
## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.92
## G.6: 0.92
## Omega Hierarchical: 0.92
## Omega H asymptotic: 1
## Omega Total 0.92
##
## Schmid Leiman Factor loadings greater than 0.2
## g F1* h2 u2 p2
## PANAS_2 0.86 0.74 0.26 1
## PANAS_4 0.81 0.66 0.34 1
## PANAS_6 0.82 0.66 0.34 1
## PANAS_8 0.88 0.78 0.22 1
## PANAS_10 0.83 0.69 0.31 1
##
## With Sums of squares of:
## g F1*
## 3.5 0.0
##
## general/max Inf max/min = NaN
## mean percent general = 1 with sd = 0 and cv of 0
## Explained Common Variance of the general factor = 1
##
## The degrees of freedom are 5 and the fit is 0.24
## The number of observations was 434 with Chi Square = 101.85 with prob < 0.000000000000000000022
## The root mean square of the residuals is 0.05
## The df corrected root mean square of the residuals is 0.07
## RMSEA index = 0.211 and the 10 % confidence intervals are 0.177 0.248
## BIC = 71.48
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 5 and the fit is 0.24
## The number of observations was 434 with Chi Square = 101.85 with prob < 0.000000000000000000022
## The root mean square of the residuals is 0.05
## The df corrected root mean square of the residuals is 0.07
##
## RMSEA index = 0.211 and the 10 % confidence intervals are 0.177 0.248
## BIC = 71.48
##
## Measures of factor score adequacy
## g F1*
## Correlation of scores with factors 0.96 0
## Multiple R square of scores with factors 0.93 0
## Minimum correlation of factor score estimates 0.85 -1
##
## Total, General and Subset omega for each subset
## g F1*
## Omega total for total scores and subscales 0.92 0.92
## Omega general for total scores and subscales 0.92 0.92
## Omega group for total scores and subscales 0.00 0.00
In this section, we will: (a) test assumptions of normality, (b) transform variables violating assumptions, (c) test assumptions of homoscedasticity, (d) identify and winsorize outliers, and (e) conduct the t-tests.
lapply(col.list, function(x)
nice_normality(data,
variable = x,
title = x,
group = "condition",
shapiro = TRUE,
histogram = TRUE))## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
##
## [[6]]
##
## [[7]]
##
## [[8]]
##
## [[9]]
##
## [[10]]
##
## [[11]]
##
## [[12]]
##
## [[13]]
Several variables are clearly skewed. Let’s apply transformations. But first, let’s deal with the working memory task, SOPT (Self-Ordered Pointing Task). It is clearly problematic.
The function below transforms variables according to the best
possible transformation (via the bestNormalize package),
and also standardizes the variables.
predict_bestNormalize <- function(var) {
x <- bestNormalize(var, standardize = FALSE, allow_orderNorm = FALSE)
print(cur_column())
print(x$chosen_transform)
cat("\n")
predict(x)
}
set.seed(100)
data <- data %>%
mutate(across(all_of(col.list),
predict_bestNormalize,
.names = "{.col}.t"))## [1] "blastintensity"
## I(x) Transformation with 434 nonmissing obs.
##
## [1] "blastduration"
## I(x) Transformation with 434 nonmissing obs.
##
## [1] "blastintensity.duration"
## Non-Standardized sqrt(x + a) Transformation with 434 nonmissing obs.:
## Relevant statistics:
## - a = 0
## - mean (before standardization) = 69.27661
## - sd (before standardization) = 31.3388
##
## [1] "KIMS"
## Non-Standardized Log_b(x + a) Transformation with 434 nonmissing obs.:
## Relevant statistics:
## - a = 0
## - b = 10
## - mean (before standardization) = 0.5285868
## - sd (before standardization) = 0.06011001
##
## [1] "BSCS"
## Non-Standardized Yeo-Johnson Transformation with 434 nonmissing obs.:
## Estimated statistics:
## - lambda = 1.496462
## - mean (before standardization) = 5.736007
## - sd (before standardization) = 1.73796
##
## [1] "BAQ"
## Non-Standardized sqrt(x + a) Transformation with 434 nonmissing obs.:
## Relevant statistics:
## - a = 0
## - mean (before standardization) = 1.738352
## - sd (before standardization) = 0.3124039
##
## [1] "SHS"
## Non-Standardized Box Cox Transformation with 434 nonmissing obs.:
## Estimated statistics:
## - lambda = -0.9999576
## - mean (before standardization) = 0.2769907
## - sd (before standardization) = 0.2598753
##
## [1] "SHS.mean"
## Non-Standardized Box Cox Transformation with 434 nonmissing obs.:
## Estimated statistics:
## - lambda = -0.9999576
## - mean (before standardization) = 0.244397
## - sd (before standardization) = 0.261915
##
## [1] "SHS.aggravation"
## Non-Standardized Box Cox Transformation with 434 nonmissing obs.:
## Estimated statistics:
## - lambda = -0.7758021
## - mean (before standardization) = 0.3430183
## - sd (before standardization) = 0.3039244
##
## [1] "PANAS_pos"
## Non-Standardized Box Cox Transformation with 434 nonmissing obs.:
## Estimated statistics:
## - lambda = 1.309279
## - mean (before standardization) = 4.799965
## - sd (before standardization) = 2.132036
##
## [1] "PANAS_neg"
## Non-Standardized sqrt(x + a) Transformation with 434 nonmissing obs.:
## Relevant statistics:
## - a = 0
## - mean (before standardization) = 1.395867
## - sd (before standardization) = 0.468339
##
## [1] "IAT"
## Non-Standardized Yeo-Johnson Transformation with 434 nonmissing obs.:
## Estimated statistics:
## - lambda = 1.213857
## - mean (before standardization) = 0.4599925
## - sd (before standardization) = 0.3868647
##
## [1] "SOPT"
## Non-Standardized Yeo-Johnson Transformation with 434 nonmissing obs.:
## Estimated statistics:
## - lambda = -0.1067761
## - mean (before standardization) = 2.178345
## - sd (before standardization) = 0.5632945
col.list <- paste0(col.list, ".t")Note. The I(x) transformations above are actually not transformations, but a shorthand function for passing the data “as is”. Suggesting the package estimated the various attempted transformations did not improve normality in those cases, so no transformation is used. This only appears when standardize is set to FALSE. When set to TRUE, for those variables, it is actually center_scale(x), suggesting that the data are only CENTERED because they need no transformation (no need to be scaled), only to be centered.
Let’s check if normality was corrected.
# Group normality
lapply(col.list, function(x)
nice_normality(data,
x,
"condition",
shapiro = TRUE,
title = x,
histogram = TRUE))## [[1]]
##
## [[2]]
##
## [[3]]
##
## [[4]]
##
## [[5]]
##
## [[6]]
##
## [[7]]
##
## [[8]]
##
## [[9]]
##
## [[10]]
##
## [[11]]
##
## [[12]]
##
## [[13]]
Looks rather reasonable now, though not perfect (fortunately t-tests are quite robust against violations of normality).
We can now resume with the next step: checking variance.
# Plotting variance
plots(lapply(col.list, function(x) {
nice_varplot(data, x, group = "condition")
}),
n_columns = 3)Variance looks good. No group has four times the variance of any other group. We can now resume with checking outliers.
We check outliers visually with the plot_outliers
function, which draws red lines at +/- 3 median absolute deviations.
plots(lapply(col.list, function(x) {
plot_outliers(data, x, group = "condition", ytitle = x, binwidth = 0.15)
}),
n_columns = 2)There are some outliers, but nothing unreasonable. Let’s still check with the 3 median absolute deviations (MAD) method.
data %>%
filter(condition == "Control") %>%
find_mad(col.list, criteria = 3)## 49 outlier(s) based on 3 median absolute deviations for variable(s):
## blastintensity.t, blastduration.t, blastintensity.duration.t, KIMS.t, BSCS.t, BAQ.t, SHS.t, SHS.mean.t, SHS.aggravation.t, PANAS_pos.t, PANAS_neg.t, IAT.t, SOPT.t
##
## The following participants were considered outliers for more than one variable:
##
## Row n
## 1 4 3
## 2 21 2
## 3 45 3
## 4 59 2
## 5 71 2
## 6 72 2
## 7 98 2
## 8 149 2
## 9 169 2
## 10 178 2
## 11 189 3
## 12 194 2
## 13 199 2
##
## Outliers per variable:
##
## $SHS.mean.t
## Row SHS.mean.t_mad
## 1 4 3.419054
## 2 45 3.079263
## 3 59 3.004612
## 4 62 3.372535
## 5 71 3.322570
## 6 72 3.268761
## 7 98 3.179813
## 8 100 3.147693
## 9 110 3.296173
## 10 148 3.372535
## 11 149 3.042767
## 12 169 3.419054
## 13 178 3.042767
## 14 189 3.268761
## 15 194 3.147693
## 16 199 3.004612
##
## $PANAS_neg.t
## Row PANAS_neg.t_mad
## 1 4 4.810718
## 2 8 3.875967
## 3 9 3.709650
## 4 21 3.539830
## 5 45 4.957393
## 6 49 4.661657
## 7 59 3.709650
## 8 64 3.366276
## 9 71 4.038990
## 10 72 4.198906
## 11 76 3.875967
## 12 92 3.188731
## 13 97 4.355888
## 14 98 4.510091
## 15 104 4.038990
## 16 133 3.709650
## 17 149 5.244022
## 18 156 3.366276
## 19 158 3.366276
## 20 160 5.384174
## 21 166 4.038990
## 22 169 4.510091
## 23 171 3.539830
## 24 178 4.038990
## 25 187 3.188731
## 26 189 3.539830
## 27 194 4.038990
## 28 199 4.355888
## 29 205 4.810718
## 30 211 4.198906
##
## $IAT.t
## Row IAT.t_mad
## 1 60 3.096102
##
## $SOPT.t
## Row SOPT.t_mad
## 1 4 3.695020
## 2 21 3.239544
## 3 33 -3.895389
## 4 36 -3.895389
## 5 43 3.678657
## 6 45 -3.895389
## 7 57 3.628411
## 8 78 3.695020
## 9 79 -3.895389
## 10 84 -3.895389
## 11 94 3.662104
## 12 96 3.695020
## 13 111 3.678657
## 14 139 3.695020
## 15 147 3.662104
## 16 155 -3.895389
## 17 189 3.695020
## 18 208 3.695020
data %>%
filter(condition == "Mindfulness") %>%
find_mad(col.list, criteria = 3)## 43 outlier(s) based on 3 median absolute deviations for variable(s):
## blastintensity.t, blastduration.t, blastintensity.duration.t, KIMS.t, BSCS.t, BAQ.t, SHS.t, SHS.mean.t, SHS.aggravation.t, PANAS_pos.t, PANAS_neg.t, IAT.t, SOPT.t
##
## The following participants were considered outliers for more than one variable:
##
## Row n
## 1 84 2
## 2 106 2
## 3 162 2
##
## Outliers per variable:
##
## $KIMS.t
## Row KIMS.t_mad
## 1 73 3.061291
## 2 175 -3.149689
##
## $PANAS_neg.t
## Row PANAS_neg.t_mad
## 1 16 4.038990
## 2 17 4.038990
## 3 19 4.661657
## 4 28 3.875967
## 5 34 4.038990
## 6 45 3.006907
## 7 55 3.366276
## 8 57 4.510091
## 9 59 3.366276
## 10 62 4.355888
## 11 64 5.244022
## 12 81 3.539830
## 13 84 4.810718
## 14 85 3.875967
## 15 88 3.709650
## 16 93 3.366276
## 17 99 3.188731
## 18 106 5.244022
## 19 109 4.198906
## 20 111 3.006907
## 21 124 3.875967
## 22 136 4.957393
## 23 140 3.539830
## 24 144 3.006907
## 25 146 5.384174
## 26 148 3.006907
## 27 154 3.875967
## 28 162 4.198906
## 29 167 4.810718
## 30 172 3.709650
## 31 176 4.810718
## 32 178 3.188731
## 33 179 3.539830
## 34 193 3.709650
## 35 197 3.875967
## 36 205 4.355888
##
## $IAT.t
## Row IAT.t_mad
## 1 1 3.412721
##
## $SOPT.t
## Row SOPT.t_mad
## 1 84 3.183769
## 2 106 3.126376
## 3 150 -3.356414
## 4 158 -4.837137
## 5 162 -3.356414
## 6 202 3.183769
## 7 209 -3.356414
There are 49 outliers after our transformations in the control group, and 44 in the mindfulness group. That seems to be due mostly to the extreme positive skew for the negative affect scale of the PANAS.
For multivariate outliers, it is recommended to use the Minimum Covariance Determinant, a robust version of the Mahalanobis distance (MCD, Leys et al., 2019).
Leys, C., Delacre, M., Mora, Y. L., Lakens, D., & Ley, C. (2019). How to classify, detect, and manage univariate and multivariate outliers, with emphasis on pre-registration. International Review of Social Psychology, 32(1).
x <- check_outliers(na.omit(data[col.list]), method = "mcd")
x## 95 outliers detected: cases 3, 6, 10, 19, 21, 26, 28, 52, 53, 56, 70,
## 72, 73, 76, 79, 87, 93, 96, 103, 114, 126, 127, 138, 162, 167, 168, 169,
## 170, 174, 176, 177, 181, 186, 187, 188, 192, 194, 204, 207, 208, 215,
## 219, 222, 232, 234, 235, 236, 238, 240, 244, 248, 256, 258, 271, 278,
## 279, 287, 291, 292, 297, 300, 311, 314, 318, 327, 329, 332, 334, 337,
## 347, 349, 350, 351, 352, 353, 355, 356, 360, 371, 383, 386, 388, 394,
## 395, 397, 400, 401, 402, 406, 407, 410, 417, 423, 427, 431.
## - Based on the following method and threshold: mcd (34.528).
## - For variables: blastintensity.t, blastduration.t,
## blastintensity.duration.t, KIMS.t, BSCS.t, BAQ.t, SHS.t, SHS.mean.t,
## SHS.aggravation.t, PANAS_pos.t, PANAS_neg.t, IAT.t, SOPT.t.
plot(x)There are 92 multivariate outliers according to the MCD method.
This time, instead of winsorizing outliers, we attempt to exclude them to see if it makes any difference. We only exclude multivariate outliers, not univariate ones.
Visual assessment and the MAD method confirm we have some outlier values. We could ignore them but because they could have disproportionate influence on the models, one recommendation is to winsorize them by bringing the values at 3 SD. Instead of using the standard deviation around the mean, however, we use the absolute deviation around the median, as it is more robust to extreme observations. For a discussion, see:
Leys, C., Klein, O., Bernard, P., & Licata, L. (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764–766. https://doi.org/10.1016/j.jesp.2013.03.013
# Winsorize variables of interest with MAD
data <- data %>%
group_by(condition) %>%
mutate(across(all_of(col.list),
winsorize_mad,
.names = "{.col}.w")) %>%
ungroup()
col.list <- paste0(col.list, ".w")We can now standardize our variables.
data <- data %>%
mutate(across(all_of(col.list), standardize, .names = "{col}.s"))
# Update col.list
col.list <- paste0(col.list, ".s")We are now ready to compare the group condition (Control vs. Mindfulness Priming) across our different variables with the t-tests.
nice_t_test(data,
response = col.list,
group = "condition") %>%
nice_table(highlight = 0.10, width = .80)## [97mUsing Welch t-test (base R's default; cf. https://doi.org/10.5334/irsp.82).
## For the Student t-test, use `var.equal = TRUE`.
## [97m
Dependent Variable | t | df | p | d | 95% CI |
|---|---|---|---|---|---|
blastintensity.t.w.s | -0.78 | 430.20 | .437 | -0.07 | [-0.26, 0.11] |
blastduration.t.w.s | -0.22 | 431.72 | .827 | -0.02 | [-0.21, 0.17] |
blastintensity.duration.t.w.s | -0.52 | 431.17 | .605 | -0.05 | [-0.24, 0.14] |
KIMS.t.w.s | 0.10 | 430.58 | .921 | 0.01 | [-0.18, 0.20] |
BSCS.t.w.s | -0.40 | 431.62 | .689 | -0.04 | [-0.23, 0.15] |
BAQ.t.w.s | 0.99 | 431.98 | .324 | 0.09 | [-0.09, 0.28] |
SHS.t.w.s | 0.14 | 431.99 | .889 | 0.01 | [-0.17, 0.20] |
SHS.mean.t.w.s | -0.02 | 431.95 | .980 | -0.00 | [-0.19, 0.19] |
SHS.aggravation.t.w.s | 0.10 | 432.00 | .920 | 0.01 | [-0.18, 0.20] |
PANAS_pos.t.w.s | 0.22 | 428.73 | .828 | 0.02 | [-0.17, 0.21] |
PANAS_neg.t.w.s | 0.55 | 431.82 | .583 | 0.05 | [-0.14, 0.24] |
IAT.t.w.s | 2.79 | 428.90 | .006 | 0.27 | [0.08, 0.46] |
SOPT.t.w.s | -0.55 | 431.98 | .585 | -0.05 | [-0.24, 0.14] |
Interpretation: There seems to be a preexisting difference in IAT levels: the mindfulness group seems to have higher implicit aggression than the control group.
nice_violin(data,
group = "condition",
response = "blastintensity.duration.t.w.s",
comp1 = 1,
comp2 = 2,
obs = TRUE,
has.d = TRUE,
d.y = 1)Let’s extract the means and standard deviations for journal reporting.
data %>%
group_by(condition) %>%
summarize(M = mean(blastintensity.duration),
SD = sd(blastintensity.duration),
N = n()) %>%
nice_table(width = 0.40)condition | M | SD | N |
|---|---|---|---|
Control | 5,637.74 | 4,417.87 | 218 |
Mindfulness | 5,921.78 | 4,561.51 | 216 |
Let’s see if our variables don’t interact together with our experimental condition. But first, let’s test the models assumptions.
big.mod3 <- lm(blastintensity.duration.t.w.s ~ condition_dum*BSCS.t.w.s
# + condition_dum*KIMS.t.w.s + condition_dum*BAQ.t.w.s
, data = data, na.action="na.exclude")
check_model(big.mod3)big.mod1 <- lm(blastintensity.t.w.s ~ condition_dum*BSCS.t.w.s
# condition_dum*KIMS.t.w.s + condition_dum*BAQ.t.w.s
, data = data, na.action="na.exclude")
check_model(big.mod1)big.mod2 <- lm(blastduration.t.w.s ~ condition_dum*BSCS.t.w.s
# + condition_dum*KIMS.t.w.s + condition_dum*BAQ.t.w.s
, data = data, na.action="na.exclude")
check_model(big.mod2)big.mod4 <- lm(SHS.t.w.s ~ condition_dum*BSCS.t.w.s
# + condition_dum*KIMS.t.w.s + condition_dum*BAQ.t.w.s
, data = data, na.action="na.exclude")
check_model(big.mod4)big.mod5 <- lm(SHS.mean.t.w.s ~ condition_dum*BSCS.t.w.s
# + condition_dum*KIMS.t.w.s + condition_dum*BAQ.t.w.s
, data = data, na.action="na.exclude")
check_model(big.mod5)big.mod6 <- lm(SHS.aggravation.t.w.s ~ condition_dum*BSCS.t.w.s
# + condition_dum*KIMS.t.w.s + condition_dum*BAQ.t.w.s
, data = data, na.action="na.exclude")
check_model(big.mod6)big.mod7 <- lm(PANAS_pos.t.w.s ~ condition_dum*BSCS.t.w.s
# + condition_dum*KIMS.t.w.s + condition_dum*BAQ.t.w.s
, data = data, na.action="na.exclude")
check_model(big.mod7)big.mod8 <- lm(PANAS_neg.t.w.s ~ condition_dum*BSCS.t.w.s
# + condition_dum*KIMS.t.w.s + condition_dum*BAQ.t.w.s
, data = data, na.action="na.exclude")
check_model(big.mod8)All the models assumptions look pretty good overall actually, even with all these variables. The lines for linearity and homoscedasticity are a bit skewed but nothing too crazy. Let’s now look at the results.
big.mod3 %>%
nice_lm() %>%
nice_table(highlight = TRUE)Dependent Variable | Predictor | df | β | t | p | sr2 | 95% CI |
|---|---|---|---|---|---|---|---|
blastintensity.duration.t.w.s | condition_dum | 430 | 0.05 | 0.52 | .606 | .00 | [0.00, 0.01] |
BSCS.t.w.s | 430 | -0.07 | -1.09 | .276 | .00 | [0.00, 0.01] | |
condition_dum × BSCS.t.w.s | 430 | 0.16 | 1.63 | .104 | .01 | [0.00, 0.02] |
big.mod1 %>%
nice_lm() %>%
nice_table(highlight = TRUE)Dependent Variable | Predictor | df | β | t | p | sr2 | 95% CI |
|---|---|---|---|---|---|---|---|
blastintensity.t.w.s | condition_dum | 430 | 0.07 | 0.77 | .441 | .00 | [0.00, 0.01] |
BSCS.t.w.s | 430 | -0.06 | -0.88 | .377 | .00 | [0.00, 0.01] | |
condition_dum × BSCS.t.w.s | 430 | 0.15 | 1.61 | .108 | .01 | [0.00, 0.02] |
big.mod2 %>%
nice_lm() %>%
nice_table(highlight = TRUE)Dependent Variable | Predictor | df | β | t | p | sr2 | 95% CI |
|---|---|---|---|---|---|---|---|
blastduration.t.w.s | condition_dum | 430 | 0.02 | 0.22 | .824 | .00 | [0.00, 0.00] |
BSCS.t.w.s | 430 | -0.08 | -1.22 | .221 | .00 | [0.00, 0.01] | |
condition_dum × BSCS.t.w.s | 430 | 0.15 | 1.53 | .127 | .01 | [0.00, 0.02] |
list(big.mod4, big.mod5, big.mod6) %>%
nice_lm() %>%
nice_table(highlight = TRUE)Dependent Variable | Predictor | df | β | t | p | sr2 | 95% CI |
|---|---|---|---|---|---|---|---|
SHS.t.w.s | condition_dum | 430 | -0.00 | -0.01 | .995 | .00 | [0.00, 0.00] |
BSCS.t.w.s | 430 | -0.33 | -5.03 | < .001 | .05 | [0.01, 0.09] | |
condition_dum × BSCS.t.w.s | 430 | -0.01 | -0.14 | .887 | .00 | [0.00, 0.00] | |
SHS.mean.t.w.s | condition_dum | 430 | 0.01 | 0.16 | .873 | .00 | [0.00, 0.00] |
BSCS.t.w.s | 430 | -0.30 | -4.55 | < .001 | .04 | [0.01, 0.08] | |
condition_dum × BSCS.t.w.s | 430 | -0.04 | -0.43 | .665 | .00 | [0.00, 0.00] | |
SHS.aggravation.t.w.s | condition_dum | 430 | 0.00 | 0.03 | .974 | .00 | [0.00, 0.00] |
BSCS.t.w.s | 430 | -0.32 | -4.98 | < .001 | .05 | [0.01, 0.09] | |
condition_dum × BSCS.t.w.s | 430 | -0.01 | -0.13 | .900 | .00 | [0.00, 0.00] |
list(big.mod7, big.mod8) %>%
nice_lm() %>%
nice_table(highlight = TRUE)Dependent Variable | Predictor | df | β | t | p | sr2 | 95% CI |
|---|---|---|---|---|---|---|---|
PANAS_pos.t.w.s | condition_dum | 430 | -0.03 | -0.31 | .756 | .00 | [0.00, 0.00] |
BSCS.t.w.s | 430 | 0.21 | 3.08 | .002 | .02 | [0.00, 0.05] | |
condition_dum × BSCS.t.w.s | 430 | 0.02 | 0.18 | .856 | .00 | [0.00, 0.00] | |
PANAS_neg.t.w.s | condition_dum | 430 | -0.04 | -0.44 | .660 | .00 | [0.00, 0.00] |
BSCS.t.w.s | 430 | -0.28 | -4.38 | < .001 | .04 | [0.00, 0.07] | |
condition_dum × BSCS.t.w.s | 430 | -0.10 | -1.12 | .262 | .00 | [0.00, 0.01] |
Interpretation: The condition by trait self-control (brief self-control scale, BSCS) interaction does not come up.
Let’s plot the main interaction(s).
interact_plot(big.mod3, pred = "condition_dum", modx = "BSCS.t.w.s",
modxvals = NULL, interval = TRUE, x.label = "condition_dum",
pred.labels = c("Control", "Mindfulness"),
legend.main = "Trait Self-Control")Interpretation: It appears that there are no interactions.
Let’s look at the simple slopes now (only for the significant interaction).
big.mod3 %>%
nice_lm_slopes(predictor = "condition_dum",
moderator = "BSCS.t.w.s") %>%
nice_table(highlight = TRUE)Dependent Variable | Predictor (+/-1 SD) | df | β | t | p | sr2 | 95% CI |
|---|---|---|---|---|---|---|---|
blastintensity.duration.t.w.s | condition_dum (LOW-BSCS.t.w.s) | 430 | -0.11 | -0.79 | .430 | .00 | [0.00, 0.01] |
condition_dum (MEAN-BSCS.t.w.s) | 430 | 0.05 | 0.52 | .606 | .00 | [0.00, 0.01] | |
condition_dum (HIGH-BSCS.t.w.s) | 430 | 0.21 | 1.52 | .130 | .01 | [0.00, 0.02] |
big.mod1 %>%
nice_lm_slopes(predictor = "condition_dum",
moderator = "BSCS.t.w.s") %>%
nice_table(highlight = TRUE)Dependent Variable | Predictor (+/-1 SD) | df | β | t | p | sr2 | 95% CI |
|---|---|---|---|---|---|---|---|
blastintensity.t.w.s | condition_dum (LOW-BSCS.t.w.s) | 430 | -0.08 | -0.59 | .553 | .00 | [0.00, 0.01] |
condition_dum (MEAN-BSCS.t.w.s) | 430 | 0.07 | 0.77 | .441 | .00 | [0.00, 0.01] | |
condition_dum (HIGH-BSCS.t.w.s) | 430 | 0.23 | 1.68 | .093 | .01 | [0.00, 0.02] |
big.mod2 %>%
nice_lm_slopes(predictor = "condition_dum",
moderator = "BSCS.t.w.s") %>%
nice_table(highlight = TRUE)Dependent Variable | Predictor (+/-1 SD) | df | β | t | p | sr2 | 95% CI |
|---|---|---|---|---|---|---|---|
blastduration.t.w.s | condition_dum (LOW-BSCS.t.w.s) | 430 | -0.13 | -0.92 | .357 | .00 | [0.00, 0.01] |
condition_dum (MEAN-BSCS.t.w.s) | 430 | 0.02 | 0.22 | .824 | .00 | [0.00, 0.00] | |
condition_dum (HIGH-BSCS.t.w.s) | 430 | 0.17 | 1.24 | .216 | .00 | [0.00, 0.01] |
Interpretation: There seems to have no effect of priming mindfulness on blast intensity as a function of self-control.
Let’s see if our variables don’t interact together with our experimental condition. But first, let’s test the models assumptions.
big.mod3 <- lm(blastintensity.duration.t.w.s ~ condition_dum*KIMS.t.w.s +
condition_dum*BSCS.t.w.s + condition_dum*BAQ.t.w.s +
condition_dum*SOPT.t.w.s + condition_dum*IAT.t.w.s
, data = data, na.action="na.exclude")
check_model(big.mod3)big.mod1 <- lm(blastintensity.t.w.s ~ condition_dum*KIMS.t.w.s +
condition_dum*BSCS.t.w.s + condition_dum*BAQ.t.w.s +
condition_dum*SOPT.t.w.s + condition_dum*IAT.t.w.s
, data = data, na.action="na.exclude")
check_model(big.mod1)big.mod2 <- lm(blastduration.t.w.s ~ condition_dum*KIMS.t.w.s +
condition_dum*BSCS.t.w.s + condition_dum*BAQ.t.w.s +
condition_dum*SOPT.t.w.s + condition_dum*IAT.t.w.s
, data = data, na.action="na.exclude")
check_model(big.mod2)big.mod4 <- lm(SHS.t.w.s ~ condition_dum*KIMS.t.w.s +
condition_dum*BSCS.t.w.s + condition_dum*BAQ.t.w.s +
condition_dum*SOPT.t.w.s + condition_dum*IAT.t.w.s
, data = data, na.action="na.exclude")
check_model(big.mod4)big.mod5 <- lm(SHS.mean.t.w.s ~ condition_dum*KIMS.t.w.s +
condition_dum*BSCS.t.w.s + condition_dum*BAQ.t.w.s +
condition_dum*SOPT.t.w.s + condition_dum*IAT.t.w.s
, data = data, na.action="na.exclude")
check_model(big.mod5)big.mod6 <- lm(SHS.aggravation.t.w.s ~ condition_dum*KIMS.t.w.s +
condition_dum*BSCS.t.w.s + condition_dum*BAQ.t.w.s +
condition_dum*SOPT.t.w.s + condition_dum*IAT.t.w.s
, data = data, na.action="na.exclude")
check_model(big.mod6)big.mod7 <- lm(PANAS_pos.t.w.s ~ condition_dum*KIMS.t.w.s +
condition_dum*BSCS.t.w.s + condition_dum*BAQ.t.w.s +
condition_dum*SOPT.t.w.s + condition_dum*IAT.t.w.s
, data = data, na.action="na.exclude")
check_model(big.mod7)big.mod8 <- lm(PANAS_neg.t.w.s ~ condition_dum*KIMS.t.w.s +
condition_dum*BSCS.t.w.s + condition_dum*BAQ.t.w.s +
condition_dum*SOPT.t.w.s + condition_dum*IAT.t.w.s
, data = data, na.action="na.exclude")
check_model(big.mod8)All the models assumptions look pretty good overall actually, even with all these variables. The lines for linearity and homoscedasticity are a bit skewed but nothing too crazy. Let’s now look at the results.
big.mod3 %>%
nice_lm() %>%
nice_table(highlight = TRUE)Dependent Variable | Predictor | df | β | t | p | sr2 | 95% CI |
|---|---|---|---|---|---|---|---|
blastintensity.duration.t.w.s | condition_dum | 422 | 0.06 | 0.59 | .555 | .00 | [0.00, 0.01] |
KIMS.t.w.s | 422 | 0.07 | 0.85 | .393 | .00 | [0.00, 0.01] | |
BSCS.t.w.s | 422 | 0.02 | 0.28 | .779 | .00 | [0.00, 0.00] | |
BAQ.t.w.s | 422 | 0.20 | 2.68 | .008 | .02 | [0.00, 0.04] | |
SOPT.t.w.s | 422 | 0.16 | 2.31 | .021 | .01 | [0.00, 0.03] | |
IAT.t.w.s | 422 | -0.08 | -1.12 | .261 | .00 | [0.00, 0.01] | |
condition_dum × KIMS.t.w.s | 422 | 0.03 | 0.29 | .773 | .00 | [0.00, 0.00] | |
condition_dum × BSCS.t.w.s | 422 | 0.13 | 1.15 | .249 | .00 | [0.00, 0.01] | |
condition_dum × BAQ.t.w.s | 422 | 0.06 | 0.55 | .585 | .00 | [0.00, 0.01] | |
condition_dum × SOPT.t.w.s | 422 | -0.00 | -0.01 | .993 | .00 | [0.00, 0.00] | |
condition_dum × IAT.t.w.s | 422 | 0.12 | 1.25 | .210 | .00 | [0.00, 0.01] |
big.mod1 %>%
nice_lm() %>%
nice_table(highlight = TRUE)Dependent Variable | Predictor | df | β | t | p | sr2 | 95% CI |
|---|---|---|---|---|---|---|---|
blastintensity.t.w.s | condition_dum | 422 | 0.08 | 0.84 | .403 | .00 | [0.00, 0.01] |
KIMS.t.w.s | 422 | 0.06 | 0.81 | .418 | .00 | [0.00, 0.01] | |
BSCS.t.w.s | 422 | 0.04 | 0.46 | .646 | .00 | [0.00, 0.00] | |
BAQ.t.w.s | 422 | 0.21 | 2.73 | .007 | .02 | [0.00, 0.04] | |
SOPT.t.w.s | 422 | 0.15 | 2.16 | .031 | .01 | [0.00, 0.03] | |
IAT.t.w.s | 422 | -0.07 | -0.97 | .332 | .00 | [0.00, 0.01] | |
condition_dum × KIMS.t.w.s | 422 | 0.03 | 0.31 | .758 | .00 | [0.00, 0.00] | |
condition_dum × BSCS.t.w.s | 422 | 0.12 | 1.03 | .303 | .00 | [0.00, 0.01] | |
condition_dum × BAQ.t.w.s | 422 | 0.03 | 0.24 | .810 | .00 | [0.00, 0.00] | |
condition_dum × SOPT.t.w.s | 422 | 0.03 | 0.31 | .760 | .00 | [0.00, 0.00] | |
condition_dum × IAT.t.w.s | 422 | 0.10 | 1.05 | .294 | .00 | [0.00, 0.01] |
big.mod2 %>%
nice_lm() %>%
nice_table(highlight = TRUE)Dependent Variable | Predictor | df | β | t | p | sr2 | 95% CI |
|---|---|---|---|---|---|---|---|
blastduration.t.w.s | condition_dum | 422 | 0.03 | 0.29 | .775 | .00 | [0.00, 0.00] |
KIMS.t.w.s | 422 | 0.07 | 0.90 | .369 | .00 | [0.00, 0.01] | |
BSCS.t.w.s | 422 | 0.01 | 0.08 | .933 | .00 | [0.00, 0.00] | |
BAQ.t.w.s | 422 | 0.19 | 2.49 | .013 | .01 | [0.00, 0.03] | |
SOPT.t.w.s | 422 | 0.16 | 2.33 | .020 | .01 | [0.00, 0.03] | |
IAT.t.w.s | 422 | -0.09 | -1.22 | .223 | .00 | [0.00, 0.01] | |
condition_dum × KIMS.t.w.s | 422 | 0.02 | 0.17 | .868 | .00 | [0.00, 0.00] | |
condition_dum × BSCS.t.w.s | 422 | 0.14 | 1.25 | .214 | .00 | [0.00, 0.01] | |
condition_dum × BAQ.t.w.s | 422 | 0.09 | 0.85 | .395 | .00 | [0.00, 0.01] | |
condition_dum × SOPT.t.w.s | 422 | -0.03 | -0.31 | .759 | .00 | [0.00, 0.00] | |
condition_dum × IAT.t.w.s | 422 | 0.13 | 1.30 | .193 | .00 | [0.00, 0.01] |
list(big.mod4, big.mod5, big.mod6) %>%
nice_lm() %>%
nice_table(highlight = TRUE)Dependent Variable | Predictor | df | β | t | p | sr2 | 95% CI |
|---|---|---|---|---|---|---|---|
SHS.t.w.s | condition_dum | 422 | -0.02 | -0.27 | .791 | .00 | [0.00, 0.00] |
KIMS.t.w.s | 422 | -0.23 | -3.41 | .001 | .02 | [0.00, 0.04] | |
BSCS.t.w.s | 422 | -0.09 | -1.28 | .202 | .00 | [0.00, 0.01] | |
BAQ.t.w.s | 422 | 0.25 | 3.70 | < .001 | .02 | [0.00, 0.05] | |
SOPT.t.w.s | 422 | 0.11 | 1.85 | .065 | .01 | [0.00, 0.02] | |
IAT.t.w.s | 422 | -0.11 | -1.73 | .085 | .01 | [0.00, 0.02] | |
condition_dum × KIMS.t.w.s | 422 | 0.04 | 0.41 | .681 | .00 | [0.00, 0.00] | |
condition_dum × BSCS.t.w.s | 422 | -0.02 | -0.20 | .844 | .00 | [0.00, 0.00] | |
condition_dum × BAQ.t.w.s | 422 | 0.01 | 0.11 | .911 | .00 | [0.00, 0.00] | |
condition_dum × SOPT.t.w.s | 422 | 0.08 | 0.98 | .329 | .00 | [0.00, 0.01] | |
condition_dum × IAT.t.w.s | 422 | 0.02 | 0.28 | .783 | .00 | [0.00, 0.00] | |
SHS.mean.t.w.s | condition_dum | 422 | -0.01 | -0.06 | .952 | .00 | [0.00, 0.00] |
KIMS.t.w.s | 422 | -0.22 | -3.22 | .001 | .02 | [0.00, 0.04] | |
BSCS.t.w.s | 422 | -0.06 | -0.85 | .394 | .00 | [0.00, 0.01] | |
BAQ.t.w.s | 422 | 0.25 | 3.73 | < .001 | .02 | [0.00, 0.05] | |
SOPT.t.w.s | 422 | 0.13 | 2.23 | .027 | .01 | [0.00, 0.02] | |
IAT.t.w.s | 422 | -0.11 | -1.76 | .078 | .01 | [0.00, 0.02] | |
condition_dum × KIMS.t.w.s | 422 | 0.07 | 0.73 | .465 | .00 | [0.00, 0.01] | |
condition_dum × BSCS.t.w.s | 422 | -0.05 | -0.50 | .614 | .00 | [0.00, 0.00] | |
condition_dum × BAQ.t.w.s | 422 | 0.05 | 0.53 | .598 | .00 | [0.00, 0.00] | |
condition_dum × SOPT.t.w.s | 422 | 0.09 | 1.12 | .264 | .00 | [0.00, 0.01] | |
condition_dum × IAT.t.w.s | 422 | 0.04 | 0.43 | .670 | .00 | [0.00, 0.00] | |
SHS.aggravation.t.w.s | condition_dum | 422 | -0.02 | -0.18 | .855 | .00 | [0.00, 0.00] |
KIMS.t.w.s | 422 | -0.23 | -3.29 | .001 | .02 | [0.00, 0.04] | |
BSCS.t.w.s | 422 | -0.10 | -1.46 | .144 | .00 | [0.00, 0.01] | |
BAQ.t.w.s | 422 | 0.24 | 3.43 | .001 | .02 | [0.00, 0.04] | |
SOPT.t.w.s | 422 | 0.07 | 1.18 | .237 | .00 | [0.00, 0.01] | |
IAT.t.w.s | 422 | -0.09 | -1.40 | .163 | .00 | [0.00, 0.01] | |
condition_dum × KIMS.t.w.s | 422 | 0.01 | 0.11 | .910 | .00 | [0.00, 0.00] | |
condition_dum × BSCS.t.w.s | 422 | -0.01 | -0.10 | .919 | .00 | [0.00, 0.00] | |
condition_dum × BAQ.t.w.s | 422 | -0.02 | -0.20 | .841 | .00 | [0.00, 0.00] | |
condition_dum × SOPT.t.w.s | 422 | 0.09 | 1.09 | .275 | .00 | [0.00, 0.01] | |
condition_dum × IAT.t.w.s | 422 | 0.01 | 0.14 | .891 | .00 | [0.00, 0.00] |
list(big.mod7, big.mod8) %>%
nice_lm() %>%
nice_table(highlight = TRUE)Dependent Variable | Predictor | df | β | t | p | sr2 | 95% CI |
|---|---|---|---|---|---|---|---|
PANAS_pos.t.w.s | condition_dum | 422 | -0.01 | -0.07 | .946 | .00 | [0.00, 0.00] |
KIMS.t.w.s | 422 | 0.28 | 3.76 | < .001 | .03 | [0.00, 0.06] | |
BSCS.t.w.s | 422 | 0.14 | 1.84 | .066 | .01 | [0.00, 0.02] | |
BAQ.t.w.s | 422 | 0.12 | 1.58 | .115 | .01 | [0.00, 0.02] | |
SOPT.t.w.s | 422 | 0.07 | 1.06 | .292 | .00 | [0.00, 0.01] | |
IAT.t.w.s | 422 | 0.06 | 0.88 | .377 | .00 | [0.00, 0.01] | |
condition_dum × KIMS.t.w.s | 422 | 0.00 | 0.03 | .977 | .00 | [0.00, 0.00] | |
condition_dum × BSCS.t.w.s | 422 | -0.02 | -0.16 | .876 | .00 | [0.00, 0.00] | |
condition_dum × BAQ.t.w.s | 422 | -0.02 | -0.18 | .853 | .00 | [0.00, 0.00] | |
condition_dum × SOPT.t.w.s | 422 | 0.02 | 0.19 | .851 | .00 | [0.00, 0.00] | |
condition_dum × IAT.t.w.s | 422 | -0.04 | -0.45 | .654 | .00 | [0.00, 0.00] | |
PANAS_neg.t.w.s | condition_dum | 422 | -0.04 | -0.53 | .599 | .00 | [0.00, 0.00] |
KIMS.t.w.s | 422 | -0.28 | -4.04 | < .001 | .03 | [0.00, 0.06] | |
BSCS.t.w.s | 422 | -0.06 | -0.84 | .402 | .00 | [0.00, 0.01] | |
BAQ.t.w.s | 422 | 0.24 | 3.51 | < .001 | .02 | [0.00, 0.05] | |
SOPT.t.w.s | 422 | 0.01 | 0.09 | .929 | .00 | [0.00, 0.00] | |
IAT.t.w.s | 422 | -0.04 | -0.59 | .559 | .00 | [0.00, 0.00] | |
condition_dum × KIMS.t.w.s | 422 | 0.02 | 0.23 | .815 | .00 | [0.00, 0.00] | |
condition_dum × BSCS.t.w.s | 422 | -0.11 | -1.05 | .296 | .00 | [0.00, 0.01] | |
condition_dum × BAQ.t.w.s | 422 | -0.05 | -0.55 | .586 | .00 | [0.00, 0.00] | |
condition_dum × SOPT.t.w.s | 422 | 0.15 | 1.74 | .082 | .01 | [0.00, 0.02] | |
condition_dum × IAT.t.w.s | 422 | 0.00 | 0.04 | .967 | .00 | [0.00, 0.00] |
Interpretation: The condition by trait self-control (brief self-control scale, BSCS) interaction does not come up.
Let’s plot the main significant interaction(s).
interact_plot(big.mod3, pred = "condition_dum", modx = "BSCS.t.w.s",
modxvals = NULL, interval = TRUE, x.label = "condition_dum",
pred.labels = c("Control", "Mindfulness"),
legend.main = "Trait Self-Control")interact_plot(big.mod1, pred = "condition_dum", modx = "BSCS.t.w.s",
modxvals = NULL, interval = TRUE, x.label = "condition_dum",
pred.labels = c("Control", "Mindfulness"),
legend.main = "Trait Self-Control")interact_plot(big.mod2, pred = "condition_dum", modx = "BSCS.t.w.s",
modxvals = NULL, interval = TRUE, x.label = "condition_dum",
pred.labels = c("Control", "Mindfulness"),
legend.main = "Trait Self-Control")Interpretation: The only interaction of interest here is the last two tabs, for state hostility and negative affect. Essentially, people in the mindfulness condition, who also have high working memory, had higher state hostility and negative affect than those in the control condition or those with low working memory. Vice-versa, people in the mindfulness condition, but with low working memory, had lower state hostility and negative affect. Relative to our original hypotheses, it seems like working memory has just replaced self-control.
Let’s look at the simple slopes now (only for the significant interaction).
big.mod3 %>%
nice_lm_slopes(predictor = "condition_dum",
moderator = "BSCS.t.w.s") %>%
nice_table(highlight = TRUE)Dependent Variable | Predictor (+/-1 SD) | df | β | t | p | sr2 | 95% CI |
|---|---|---|---|---|---|---|---|
blastintensity.duration.t.w.s | condition_dum (LOW-BSCS.t.w.s) | 422 | -0.07 | -0.50 | .617 | .00 | [0.00, 0.00] |
condition_dum (MEAN-BSCS.t.w.s) | 422 | 0.06 | 0.59 | .555 | .00 | [0.00, 0.01] | |
condition_dum (HIGH-BSCS.t.w.s) | 422 | 0.18 | 1.27 | .206 | .00 | [0.00, 0.01] |
big.mod1 %>%
nice_lm_slopes(predictor = "condition_dum",
moderator = "BSCS.t.w.s") %>%
nice_table(highlight = TRUE)Dependent Variable | Predictor (+/-1 SD) | df | β | t | p | sr2 | 95% CI |
|---|---|---|---|---|---|---|---|
blastintensity.t.w.s | condition_dum (LOW-BSCS.t.w.s) | 422 | -0.04 | -0.25 | .806 | .00 | [0.00, 0.00] |
condition_dum (MEAN-BSCS.t.w.s) | 422 | 0.08 | 0.84 | .403 | .00 | [0.00, 0.01] | |
condition_dum (HIGH-BSCS.t.w.s) | 422 | 0.19 | 1.33 | .183 | .00 | [0.00, 0.02] |
big.mod2 %>%
nice_lm_slopes(predictor = "condition_dum",
moderator = "BSCS.t.w.s") %>%
nice_table(highlight = TRUE)Dependent Variable | Predictor (+/-1 SD) | df | β | t | p | sr2 | 95% CI |
|---|---|---|---|---|---|---|---|
blastduration.t.w.s | condition_dum (LOW-BSCS.t.w.s) | 422 | -0.11 | -0.76 | .445 | .00 | [0.00, 0.01] |
condition_dum (MEAN-BSCS.t.w.s) | 422 | 0.03 | 0.29 | .775 | .00 | [0.00, 0.00] | |
condition_dum (HIGH-BSCS.t.w.s) | 422 | 0.17 | 1.14 | .255 | .00 | [0.00, 0.01] |
big.mod4 %>%
nice_lm_slopes(predictor = "condition_dum",
moderator = "SOPT.t.w.s") %>%
nice_table(highlight = TRUE)Dependent Variable | Predictor (+/-1 SD) | df | β | t | p | sr2 | 95% CI |
|---|---|---|---|---|---|---|---|
SHS.t.w.s | condition_dum (LOW-SOPT.t.w.s) | 422 | -0.10 | -0.88 | .379 | .00 | [0.00, 0.01] |
condition_dum (MEAN-SOPT.t.w.s) | 422 | -0.02 | -0.27 | .791 | .00 | [0.00, 0.00] | |
condition_dum (HIGH-SOPT.t.w.s) | 422 | 0.06 | 0.51 | .611 | .00 | [0.00, 0.00] |
big.mod8 %>%
nice_lm_slopes(predictor = "condition_dum",
moderator = "SOPT.t.w.s") %>%
nice_table(highlight = TRUE)Dependent Variable | Predictor (+/-1 SD) | df | β | t | p | sr2 | 95% CI |
|---|---|---|---|---|---|---|---|
PANAS_neg.t.w.s | condition_dum (LOW-SOPT.t.w.s) | 422 | -0.20 | -1.61 | .108 | .00 | [0.00, 0.02] |
condition_dum (MEAN-SOPT.t.w.s) | 422 | -0.04 | -0.53 | .599 | .00 | [0.00, 0.00] | |
condition_dum (HIGH-SOPT.t.w.s) | 422 | 0.11 | 0.87 | .385 | .00 | [0.00, 0.01] |
Interpretation: There seems to have no effect of priming mindfulness on blast intensity as a function of self-control.
Based on the results, it seems that the predicted interaction between self-control and the priming mindfulness manipulation does not come up. The exploratory analyses including the larger models also did not show the expected effects.
Previously, the results revealed an interaction between the condition and working memory (SOPT) on state hostility, its two subscales, and negative affect. However, this was due to an error, as the non-transformed, non-winsorized, non-standardized variable was used. Now that we use the proper variable, no interaction comes up.
report::cite_packages(sessionInfo())